A self-organizing map with expanding force for data clustering and visualization

Wing Ho SHUM, Hui Dong JIN, Kwong Sak LEUNG, Man Leung WONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

4 Citations (Scopus)

Abstract

The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to detect and preserve better topology correspondence between the two spaces. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM in terms of both the topological and the quantization errors. Furthermore, clustering results generated by the ESOM are more accurate than those by the SOM.
Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages434-441
Number of pages8
DOIs
Publication statusPublished - 1 Jan 2002

Fingerprint Dive into the research topics of 'A self-organizing map with expanding force for data clustering and visualization'. Together they form a unique fingerprint.

  • Cite this

    SHUM, W. H., JIN, H. D., LEUNG, K. S., & WONG, M. L. (2002). A self-organizing map with expanding force for data clustering and visualization. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 434-441) https://doi.org/10.1109/ICDM.2002.1183939